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The Race For Data Quality in a Medallion Architecture

DataKitchen

This architecture is valuable for organizations dealing with large volumes of diverse data sources, where maintaining accuracy and accessibility at every stage is a priority. It sounds great, but how do you prove the data is correct at each layer? How do you ensure data quality in every layer ?

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Data Integrity, the Basis for Reliable Insights

Sisense

Uncomfortable truth incoming: Most people in your organization don’t think about the quality of their data from intake to production of insights. However, as a data team member, you know how important data integrity (and a whole host of other aspects of data management) is. What is data integrity?

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Data’s dark secret: Why poor quality cripples AI and growth

CIO Business Intelligence

We also examine how centralized, hybrid and decentralized data architectures support scalable, trustworthy ecosystems. As data-centric AI, automated metadata management and privacy-aware data sharing mature, the opportunity to embed data quality into the enterprises core has never been more significant.

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Companies to shift AI goals in 2025 — with setbacks inevitable, Forrester predicts

CIO Business Intelligence

The challenge is that these architectures are convoluted, requiring diverse and multiple models, sophisticated retrieval-augmented generation stacks, advanced data architectures, and niche expertise,” they said. They predicted more mature firms will seek help from AI service providers and systems integrators.

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How HPE Aruba Supply Chain optimized cost and performance by migrating to an AWS modern data architecture

AWS Big Data

This post describes how HPE Aruba automated their Supply Chain management pipeline, and re-architected and deployed their data solution by adopting a modern data architecture on AWS. The new solution has helped Aruba integrate data from multiple sources, along with optimizing their cost, performance, and scalability.

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Improve Business Agility by Hiring a DataOps Engineer

DataKitchen

DataOps Engineers implement the continuous deployment of data analytics. They give data scientists tools to instantiate development sandboxes on demand. They automate the data operations pipeline and create platforms used to test and monitor data from ingestion to published charts and graphs.

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5 Ways Data Modeling Is Critical to Data Governance

erwin

Deploying higher quality data sources with the appropriate structural veracity: Automate and enforce data model design tasks to ensure data integrity. From regulatory compliance and business intelligence to target marketing, data modeling maintains an automated connection back to the source.